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US Patent 11263707 Machine learning in agricultural planting, growing, and harvesting contexts

Patent 11263707 was granted and assigned to Indigo on March, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Indigo
Indigo
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Current Assignee
Indigo
Indigo
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
112637070
Patent Inventor Names
Carlos Becco0
Charles David Brummitt0
Chris Moscardini0
Dan Creagh0
Daniel Bachner0
David Patrick Perry0
David Weisman0
Eric Michael Jeck0
...
Date of Patent
March 1, 2022
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Patent Application Number
160573870
Date Filed
August 7, 2018
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Patent Citations
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US Patent 10076120 Seed-origin endophyte populations, compositions, and methods of use
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US Patent 10058101 Methods of use of seed-origin endophyte populations
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US Patent 10564316 Forecasting national crop yield during the growing season
Patent Citations Received
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US Patent 11710196 Information translation in an online agricultural system
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US Patent 11748984 System and method for horticulture viability prediction and display
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US Patent 11776071 Machine learning in agricultural planting, growing, and harvesting contexts
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US Patent 11880894 Systems and methods for ecosystem credit recommendations
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US Patent 11915329 Interaction management in an online agricultural system
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US Patent 11913928 System for estimating amount of absorbed carbon dioxide and method for estimating amount of absorbed carbon dioxide
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US Patent 11978072 Systems for management of location-aware market data
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US Patent 12008663 Comprehensive multi-criteria multi-objective economic analysis tool for growing crops
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...
Patent Primary Examiner
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Matthew C. Bella
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Patent abstract

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

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